基于拼接缝自适应消除和全景图矫直的快速图像拼接算法

针对图像拼接存在色差过渡不均匀、图像倾斜扭曲及拼接效率低的现象，提出一种基于拼接缝自适应消除和全景图矫直的快速图像拼接算法。首先，用尺度不变特征变换（SIFT）提取图像指定区域特征点并用双向K近邻（KNN）算法进行图像配准，有效提高算法效率；其次，利用动态规划思想提出自适应公式找到最优拼接缝并用图像融合算法对其自适应消除，解决拼接缝色差过渡不均匀问题；最后，针对累积拼接误差形成全景图倾斜的现象，利用边缘检测算法提出自适应拟合四边形矫直模型，把原始全景图矫直为一个全新的全景图。所提算法与分块图像拼接和二叉树图像拼接算法相比，图像质量提升了5.84%~7.83%，拼接时间仅为原来的50%~70%。实验结果表明，该算法不仅通过自适应更新机制减少不同图像背景下拼接缝色差过渡不均匀的现象，从而提高了图像质量；而且提高了拼接效率，降低了全景图倾斜扭曲程度。     

Texture Feature Extraction Method for Ground Nephogram Based on Contourlet and the Power Spectrum Analysis Algorithm

It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection. In this paper, a new method is presented to capture the contour edge, texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm. More abundant texture information is extracted. Cloud images can be obtained a multiscale and multidirection decomposition. The coefficient matrix from Contourlet transform of ground nephogram is calculated. The energy, mean and variance characteristics calculated from coefficient matrix are composed of the feature information. The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis. Then Support Vector Machines (SVM) classifier is used to classify according to the frequency information of the trend graph of data series. It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds.     

基于高光谱图像的改进SIFT特征提取与匹配

针对尺度不变特征变换(SIFT)算法所提取图像特征点数量少、误匹率高的问题,提出了一种基于高光谱图像的改进SIFT算法。首先,依据传统SIFT算法中高斯金字塔的构造思想,结合在不同波段下的高光谱图像具有相同宏观特征的特点,首次用高光谱图像作为原始算法中经高斯变换产生的图像,使得检测到的具有实际意义的特征点数量大幅增加;其次,传统SIFT算法以及大量的改进方法都只通过目标象元邻域范围内的像素信息来构造特征描述符,而忽略了像素点的位置信息,文中将目标象元的位置信息纳入了特征描述符,在特征描述符的匹配阶段,在利用邻域范围内的像素信息进行粗匹配之后,利用特征描述符中的位置信息进行精细匹配。仿真实验结果表明在限定最优值与次优值之比的情况下,采用高光谱图像构造高斯金字塔的方式能显著增加特征点的提取数量,更多地挖掘出图像中的极值点;在特征描述符中加入目标象元的位置信息作为特征点匹配第二阶段的判断依据,正确匹配数量达到原方法的59倍以上,极大提升了算法的匹配性能。     

基于多项式变换的参数估计方法

本文给出一种采用DPT估计SNCK信号时宽—带宽积的方法,并通过仿真该估计方法的性能与其它估计方法进行比较.首先给出SNCK信号参数估计的一般过程.为了便于计算和理论推导,根据估计出的中心频率将接收到的SNCK信号搬移到零频,从而进一步估计其它参数,如采用DPT估计SNCK信号时宽带宽积.本文将重点研究采用DPT算法估计SNCK信号值的方法.     

Performance evaluation of noise reduction method during on-line monitoring of MV switchgear for PD measurements by non-intrusive sensors

Partial Discharge (PD) measurement is a globally accepted method for insulation diagnosis of electrical assets. The consequences of insulation breakdown are well known. The trend is to move from conventional offline testing to online monitoring for insulation life prediction, which results in the inclusion of high frequency noise in the captured signals. Therefore de-noising is of paramount importance in online monitoring to obtain useful information from the signal.In this research, a 20 kV switchgear panel has been subjected to PD faults in the laboratory and measurements have been carried out by using different non-intrusive sensors including a novel sensor, the D-dot sensor and recorded by a high frequency oscilloscope. The measured results show the effective applicability of sensors for switchgear. The Discrete Wavelet Transform (DWT) has been used to de-noise PD signals in this paper. Time domain and frequency domain comparison of original and de-noised PD signals reveals the significance of this technique for online monitoring of Medium Voltage (MV) switchgear. Finally, an adaptive online de-noising concept, based on automatic de-noising is also proposed in this paper.     

Evaluation of particle recovery from microalgae

Coccoliths are micro-structured biomineral particles found in cell protective covering layers of coccolithophore species. They are mainly composed of CaCO₃ and their individual crystal entities are arranged in such a way that they construct complex and unique structures. This complexity is found down to the individual particle level and appears to have promising properties to offer. This study focuses on the essential step prior to any kind of implementation, which is the recovery of the material. It summarizes cleaning protocols found in literature, compares them for the first time for the same freshly cultivated material and addresses challenges that still need to be overcome. Further, it highlight the advantages and disadvantages of the best cleaning protocols, suggests optimizations with promising results and uses size distribution measurements to analyse the recovery efficiency. To that end, further characterization techniques, new for coccoliths, are introduced and used to improve our current knowledge of the particles behaviour.     

采用改进尺度不变特征变换在多变背景下实现快速目标识别

提出一种改进的尺度不变特征变换(SIFT)算法,用于实现多变背景下的快速目标识别。首先,构建目标图像尺度空间,提取SIFT特征点并将其按大小分类,目标识别时只需比较同一类型的特征点。然后,由SIFT特征点子区域方向直方图计算得到4个新角度用于代表特征点的方向信息,并且在目标识别时根据角度信息限制特征点匹配范围,从而提高SIFT算法的运算速度。最后,计算目标图像和待识别图像之间的尺度因子,在尺度因子约束条件下进行目标特征点匹配,从而有效地保证正确匹配数量,提高目标识别的鲁棒性。实验结果表明:当目标在待识别图像中发生局部遮挡、旋转、尺度变化或者弱光照等情况下,改进的SIFT算法能够完成多变背景下快速目标识别任务,平均识别速度提升了40%。     

基于功率谱熵的频谱感知算法研究

针对低信噪比条件下认知无线电频谱感知问题,提出了一种基于功率谱熵的频谱检测算法。在分析主用户信号空闲与占用两种不同条件下观测信号功率谱熵差异的基础上,将其作为检验统计量,并确定了相应的判决门限,以实现对主用户信号频谱是否空闲的判决。计算机仿真结果表明,本算法无需信号的先验信息,可在较低信噪比条件下实现对常用调制信号的频谱感知,与盒维数频谱感知方法相比,检测性能约有8 dB的改进。     

自适应收缩函数的Contourlet变换图像去噪方法

针对传统图像在去噪过程中存在丢失细节且去噪效果不理想的情况,提出一种自适应收缩函数的Contourlet变换图像去噪方法.该方法利用Contourlet变换的基本形式,结合软阈值和硬阈值收缩函数的优点定义自适应收缩函数,并将其应用于图像去噪.实验结果表明,所提出的方法能有效消除噪点,图像的峰值信噪比及增强因子等图像质量指标有明显地提高,去噪后图像的视觉效果良好.